Classification of petroleum well drilling operations with a hybrid particle swarm/ant colony algorithm

Serapião, Adriane B. S.; Mendes, José Ricardo P.

Otro

Registro en:

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5579 LNAI, p. 301-310.

0302-9743

1611-3349

http://hdl.handle.net/11449/71234

http://acervodigital.unesp.br/handle/11449/71234

10.1007/978-3-642-02568-6_31

WOS:000269972300031

2-s2.0-70350633099

http://dx.doi.org/10.1007/978-3-642-02568-6_31

http://repositorioslatinoamericanos.uchile.cl/handle/2250/892240

Autor

Serapião, Adriane B. S.

Mendes, José Ricardo P.

Institución

Universidade Paulista Júlio de Mesquita Filho (Brasil)

Resumen

This paper describes an investigation of the hybrid PSO/ACO algorithm to classify automatically the well drilling operation stages. The method feasibility is demonstrated by its application to real mud-logging dataset. The results are compared with bio-inspired methods, and rule induction and decision tree algorithms for data mining. © 2009 Springer Berlin Heidelberg.

Materias

Bio-inspired

Colony algorithms

Data sets

Decision-tree algorithm

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